Abstract: Schizophrenia is a devastating illness, and dysfunction of the prefrontal cortex (PFC) is widely believed to underlie cognitive impairment and other debilitating aspects of this disorder. Current treatments are limited at best, and although we have identified numerous genetic, molecular, cellular, and synaptic alterations related to schizophrenia, we have not been able to translate these findings into more effective treatments. The central reason for this is tha we simply do not know how circuits in the PFC (or elsewhere in the brain) work - we do not know how the properties of cells or their interactions give rise to patterns of activity that enabl these brain regions to carry out functions, including the ones impaired in psychiatric illnesses. Here we propose a novel strategy to reverse engineer recurrent circuits in the cortex, i.e. to measure their activity in a way that makes it possible to infer both their overall function, and th contributions that individual components (e.g. specific cell types) make to this function. Our approach is widely applicable, and we will demonstrate its utility by answering specific questions about the recurrent layer V network in prefrontal cortex, because dopaminergic modulation of this network plays a critical role in schizophrenia. Activity in this network may converge to stabl states or progress through a deterministic sequence of states in order to store information in working memory. Alternatively, this network may generate noisy activity that makes prefrontal output more variable in order to facilitate behavioral adaptation. Such noise could cause prefrontal dysfunction under pathological conditions. Our approach will determine which of these hypothesized functions the layer V prefrontal network carries out when various dopamine receptors are activated. In the process, we will define prefrontal noise and related concepts. We will isolate layer V from external inputs using a brain slice preparation, and record simultaneous activity from many neurons in layer V of the PFC using the genetically encoded calcium indicator GCaMP3. We present examples of network activity recording using GCaMP3, and describe how statistical methods such as Hidden Markov Models can decode this activity and infer the function of the network. We also show that layer V contains multiple subtypes of pyramidal neurons, and illustrate how we can distinguish these subtypes while recording activity with GCaMP3. Dopamine D2 receptors are only expressed in one of these subtypes, and we will test the hypothesis that D2 receptor activation generates prefrontal noise through specific effects on these neurons. Finally, we describe ways to extend our approach by combining optogenetic stimulation with GCaMP3 imaging. These experiments will answer long-standing questions about prefrontal networks and demonstrate approaches that may deconstruct networks throughout the cortex. Furthermore, by reverse engineering the black box of prefrontal circuitry that is commonly invoked to connect genetic or developmental lesions with their behavioral consequences, this study may open up fundamentally new ways of thinking about psychiatric illness, making it possible to design novel therapies that target circuit dysfunction. Public Health Relevance: Schizophrenia affects approximately 1% of the population worldwide, causing distress for patients and their families, and in most cases, life-long disabilit. Many of the most disabling symptoms of schizophrenia are thought to involve dopamine and dysfunction of the prefrontal cortex. Here we propose to study how dopamine modulates the function of the prefrontal cortex in order to identify critical mechanisms that are likely to be disrupted in schizophrenia and related conditions.